發(fā)布時(shí)間：2018-05-22 09:23

  本文選題：信息融合 + 預(yù)測(cè)　； 參考：《華南理工大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年03期

【摘要】：針對(duì)執(zhí)勤車(chē)輛沿某一路徑行駛時(shí)長(zhǎng)難以預(yù)測(cè)的問(wèn)題,文中提出一種KMP-RBF融合方法,采用GPS定位、悉尼自適應(yīng)交通控制系統(tǒng)(SCATS)線圈作為融合信號(hào)源,建立路況信息選擇融合模型,將模糊推理知識(shí)表達(dá)、MAPSO算法尋優(yōu)和RBF網(wǎng)絡(luò)訓(xùn)練相結(jié)合,自適應(yīng)優(yōu)化系統(tǒng)關(guān)鍵參數(shù),從訓(xùn)練數(shù)據(jù)庫(kù)匹配適用時(shí)間、空間數(shù)據(jù).實(shí)驗(yàn)中,使用交通監(jiān)控系統(tǒng)實(shí)測(cè)實(shí)驗(yàn)用車(chē)行駛時(shí)長(zhǎng)數(shù)據(jù),并與預(yù)測(cè)數(shù)據(jù)進(jìn)行對(duì)比,從誤差率、算法迭代與精確度方面證明文中方法是高效而可靠的.
[Abstract]:In order to solve the problem that it is difficult to predict the length of running time of an operating vehicle along a certain path, a KMP-RBF fusion method is proposed in this paper, which adopts GPS positioning and Sydney adaptive traffic control system (SCATS) coil as the fusion signal source, and establishes the road condition information selection fusion model. By combining fuzzy reasoning knowledge expression algorithm with RBF network training, the key parameters of the system are adaptively optimized to match the applicable time and spatial data from the training database. In the experiment, the traffic monitoring system is used to measure the experimental vehicle driving time data, and compared with the prediction data, the error rate, algorithm iteration and accuracy prove that the method in this paper is efficient and reliable.
【作者單位】： 天津大學(xué)電氣自動(dòng)化與信息工程學(xué)院;天津市公安消防局;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61201179) 國(guó)家博士后科學(xué)基金資助項(xiàng)目(2016M601265)~~
【分類(lèi)號(hào)】：TP18;U491

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李俊;田勇亮;吳忠林;;行車(chē)時(shí)間估計(jì)和最佳路由選擇的數(shù)學(xué)模型[J];天中學(xué)刊;2010年02期

2 王耀;黃立波;劉紅軍;成禮智;;帶時(shí)空相關(guān)性分析的行車(chē)時(shí)間估計(jì)模型[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2006年07期

3 汪曉妍;李凡希;李艷杰;;行車(chē)時(shí)間估計(jì)與路徑尋優(yōu)問(wèn)題[J];數(shù)學(xué)的實(shí)踐與認(rèn)識(shí);2006年07期

4 王非;;滬陜高速公路陜西境全線通車(chē)[J];西部大開(kāi)發(fā);2008年11期

5 趙玲;王佳t，

本文編號(hào)：1921498